We propose a new approach to the study of inferential learning by investigating how monkeys and humans infer implicit serial relationships during training on a Transitive Inference (TI) paradigm. TI implies the ability to conclude that A C if A > B and B > C. This logic can be extended to any number of items as long as their relationships obey transitivity. TI has been shown to exist in species as diverse as pigeons, monkeys, and humans and is thought to be essential for understanding complex social relationships such as dominance hierarchies. TI is also critical for understanding ordinal relationships, which, by definition, obey transitivity. Linear spatial relationships also obey transitivity (if A is to the left of B, and B is to the left of C, then A is to the left of C). Hene, it has been proposed that TI may be related to spatial representations that inhabit a virtual workspace. The idea is that one can imagine adjacent items in an ordered list as occupying neighboring positions on an imaginary line. Thus, ordinal relationships that seem abstract may in fact be mapped onto existing spatial representations. To test this, we plan to investigate the learning and representation of ordinal relationships among novel stimuli in regions of parietal and prefrontal cortex that are believed to be involved in representing spatial information, especially relative spatial position. These are the first experiments to investigate the acquisition of implict inference at the behavioral level that is synchronized to simultaneous measurement of the activity of individual brain cells throughout TI learning (including acquisition). Ours are signifiant because they provide the first neurological investigation of implicit serial learning in a non-human primate that is not confounded by spatial or temporal cues. From a physiologist's perspective, areas LIP and SEF have been shown to encode both spatial and abstract qualities of visual stimuli. There is, however, no theoretical framework that integrates these different representations. We propose to test the idea that a virtual workspace may account for both spatial and non-spatial coding in LIP and SEF. Health Relatedness: These experiments are relevant to Schizophrenia, Autism, Alzheimer's disease, and other conditions whose patient populations have deficits in their performance of TI problems.